Agent Architecture

This document details the internal architecture of a CLE-Net agent.

1. Overview

A CLE-Net agent is an autonomous cognitive unit that:

1. Observes human interaction (text, voice, documents)

2. Extracts symbolic representations from raw input

3. Discovers latent rules through symbolic regression

4. Communicates discoveries to the network

5. Learns from global consensus

Agents are designed to be:

• Independent: Each agent operates on its own data

• Resilient: Can recover from crashes and failures

• Persistent: State survives across restarts

• Evolving: Rules and knowledge improve over time

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2. Agent Components

┌─────────────────────────────────────────────────────────────────┐
│                        CLE Agent                                 │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐      │
│  │  Input       │───▶│  Event       │───▶│  Semantic    │      │
│  │  Handlers    │    │  Stream      │    │  Atomizer    │      │
│  └──────────────┘    └──────────────┘    └──────────────┘      │
│                                                  │              │
│  ┌──────────────┐    ┌──────────────┐           │              │
│  │  Consensus   │◀───│  Rule        │◀──────────┘              │
│  │  Engine      │    │  Manager     │                          │
│  └──────────────┘    └──────────────┘                          │
│         │                   │                                   │
│         │                   ▼                                   │
│         │         ┌──────────────────┐                       │
│         └─────────│  Blockchain       │                       │
│                   │  Interface        │                       │
│                   └──────────────────┘                       │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

2.1 Input Handlers

Purpose: Convert various input modalities into a unified event stream

Supported Inputs:

Modality	Handler	Output
Text	TextHandler	TextEvents
Voice	VoiceHandler	AudioEvents
Documents	DocHandler	DocumentEvents
Video	VideoHandler	FrameEvents + AudioEvents

Key Design: All handlers output to the Event Stream, ensuring temporal consistency.

2.2 Event Stream

Purpose: Maintain chronological order of all interactions

Properties:

• Ordered: Events have timestamps

• Interruptible: Handles overlapping events (for full-duplex)

• Partial: Can process incomplete events

• Persistent: Events are stored locally

Event Structure:

class Event:
    event_id: str              # Unique identifier
    timestamp: float           # When event occurred
    source: str                # Input handler origin
    modality: str              # text, voice, etc.
    speaker: Optional[str]     # Who produced it
    raw_content: Any           # Original content
    confidence: float          # Processing confidence
    metadata: Dict             # Additional info

2.3 Semantic Atomizer

Purpose: Extract meaning units (atoms) from raw events

Extracts:

Atom Type	Description	Example
Entities	Nouns, actors	“client”, “manager”
Attributes	Properties	VIP=true, delay=short
Actions	Verbs, decisions	ignore, approve
Conditions	Context	“if client is important”
Negations	Inverted meaning	“not applicable”
Probabilities	Uncertainty	“usually”, “maybe”
Temporal	Time markers	“always”, “never”

Process:

1. Parse raw content

2. Identify atom types

3. Normalize representations

4. Assign confidence scores

Example:

Input: "Usually VIP clients don't get penalties for short delays"

Atoms:
- Entity: client (type=VIP)
- Condition: delay (duration=short)
- Action: ignore_penalty
- Probability: usually (confidence=0.8)

2.4 Symbol Mapper

Purpose: Convert atoms to formal logical representations

Process:

1. Map atoms to logical predicates

2. Normalize to canonical form

3. Create rule skeleton

Example:

Atoms:
- client.VIP = true
- delay < T
- ignore_penalty = true

Canonical Rule:
IF Client.VIP = true AND Delay < threshold
THEN IgnorePenalty = true

Canonicalization Rules:

• Alphabetical ordering of conditions

• Standardized predicate names

• Normalized value representations

• Deterministic formatting

2.5 Symbolic Regression Engine

Purpose: Discover latent rules from accumulated symbols

Methods:

1. Pattern Detection: Find repeated co-occurrences

2. Rule Induction: Propose candidate rules

3. Quality Evaluation: Score rules by:

   • Coverage (how many events explained)

   • Simplicity (Occam’s razor)

   • Consistency (low contradictions)

Output: RuleCandidate objects

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2.6 Enhanced Symbolic Regression ✅ PHASE 3 - COMPLETED

Purpose: Advanced symbolic discovery using Genetic Programming (GP)

Components:

2.6.1 SymbolicExpression

• Tree-based representation of symbolic rules

• Supports operators: +, *, >, <, ==, AND, OR

• Tracks fitness, complexity, and canonical form

2.6.2 SymbolicRegressor

• Genetic Programming (GP): Evolves expression trees

  • Population-based evolution (configurable size)

  • Tournament selection for parent selection

  • Crossover and mutation operators

  • Elitism to preserve best solutions

• Fitness Function: Combines multiple metrics

  • Coverage: Fraction of events explained

  • Correlation: Statistical fit to data

  • Simplicity penalty: Prefers simpler rules

• Configuration:

  population_size: 100
  generations: 50
  mutation_rate: 0.1
  crossover_rate: 0.7
  max_depth: 5
  simplicity_weight: 0.3
  

2.6.3 TemporalPatternRecognizer

• Trend Detection: Identifies increasing/decreasing/stable patterns

• Periodicity Detection: Finds repeating patterns using autocorrelation

• Change Point Detection: CUSUM-based detection of regime changes

• Sliding Window: Configurable window size for analysis

2.6.4 UncertaintyQuantifier

• Bootstrap Confidence Intervals: Statistical bounds on predictions

• Prediction Intervals: t-distribution based bounds

• Rule Confidence: Combines coverage, consistency, and support

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2.7 Multi-Modal Input Processing ✅ PHASE 3 - COMPLETED

Purpose: Handle diverse input modalities with unified processing

2.7.1 VoiceHandler

• Speech-to-Text: Transcription of audio input

• Emotion Detection: Classifies emotional state

• Speaker Identification: Distinguishes different speakers

• Audio Features: Pitch, speech rate, pause frequency

2.7.2 VideoHandler

• Frame Extraction: Key frame sampling from video

• Scene Detection: Identifies scene boundaries

• Audio Track Processing: Extracts and processes audio

• Lip Sync: Enables full-duplex interaction

2.7.3 DocumentHandler

• OCR: Text extraction from scanned documents

• PDF Processing: Native PDF parsing

• Layout Analysis: Identifies structure (headers, tables, lists)

• Table Extraction: Extracts tabular data

• Entity Extraction: Named entity recognition

2.7.4 ImageHandler

• Object Detection: Identifies objects in images

• Scene Description: Generates textual descriptions

• Text Extraction: OCR for embedded text

• Feature Extraction: Visual embeddings

2.7.5 MultimodalProcessor

• Unified Interface: Single entry point for all modalities

• Modality Routing: Routes to appropriate handler

• Result Aggregation: Combines multi-modal outputs

2.7.6 FullDuplexController

• Simultaneous I/O: Processes input and output concurrently

• Interrupt Handling: Gracefully handles interruptions

• Real-time Processing: Low-latency processing

• State Management: Tracks listening/speaking states

RuleCandidate Structure:

class RuleCandidate:
    rule_id: str
    logic_form: str           # Canonical rule
    context: str              # Domain/context tags
    confidence: float
    coverage: float
    simplicity: float
    support_count: int
    contradiction_count: int
    evidence: List[str]       # Event IDs supporting this rule

2.6 Rule Manager

Purpose: Manage local rules and coordinate with network

Responsibilities:

1. Track all discovered rules

2. Maintain local rule state

3. Handle consensus feedback from network

4. Update confidence scores

5. Trigger rule broadcasts

Rule Lifecycle:

Discovery → Candidate → Broadcast → Consensus → Accepted
                                         ↓
                              Contradiction → Decay
                                         ↓
                              Verification → Strengthen

2.7 Consensus Engine

Purpose: Validate rules against network consensus

Process:

1. Receive rule commitments from network

2. Check independence of discoveries

3. Calculate confidence based on:

   • Number of supporting agents

   • Diversity of data sources

   • Time stability

4. Resolve conflicts

5. Update local rule state

2.8 Blockchain Interface

Purpose: Communicate with the network

Functions:

1. Broadcast: Send rule commitments

2. Subscribe: Receive network events

3. Query: Request rule state

4. Sync: Download latest consensus

---

3. Agent State

3.1 Local State Structure

class AgentState:
    agent_id: str
    node_identity: Identity
    current_rules: Dict[str, Rule]
    event_history: List[Event]
    symbol_buffer: List[Atom]
    pending_commits: List[RuleCandidate]
    consensus_state: Dict[str, float]
    last_checkpoint: int
    last_sync: float

3.2 State Persistence

Agents persist state to:

• Local disk: For crash recovery

• Network: For migration and replication

Checkpoint Strategy:

• Periodic snapshots (configurable interval)

• On significant events (rule accepted, etc.)

• Before network operations

3.3 State Migration

If an agent needs to move to a new machine:

1. Export state to encrypted package

2. Broadcast availability

3. New agent imports and verifies

4. Resume operation with full history

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4. Agent Lifecycle

┌─────────────┐
│   START     │
└──────┬──────┘
       │
       ▼
┌─────────────┐
│  INITIALIZE │
│  - Load     │
│  - Sync     │
│  - Verify   │
└──────┬──────┘
       │
       ▼
┌─────────────┐
│   OBSERVE   │◄──────────────────┐
│  - Capture  │                   │
│  - Extract  │                   │
└──────┬──────┘                   │
       │                          │
       ▼                          │
┌─────────────┐                   │
│  DISCOVER   │                   │
│  - Analyze  │                   │
│  - Propose  │                   │
└──────┬──────┘                   │
       │                          │
       ▼                          │
┌─────────────┐                   │
│  BROADCAST  │                   │
│  - Commit   │                   │
│  - Wait     │                   │
└──────┬──────┘                   │
       │                          │
       ▼                          │
┌─────────────┐                   │
│  CONSENSUS  │                   │
│  - Validate │                   │
│  - Update   │                   │
└──────┬──────┘                   │
       │                          │
       ▼                          │
┌─────────────┐                   │
│   LEARN     │                   │
│  - Adjust   │                   │
│  - Store    │                   │
└──────┬──────┘                   │
       │                          │
       ▼                          │
┌─────────────┐                   │
│   CHECK     │───── CRASH? ─────►│  RECOVER
│  - Healthy? │                   │  - Restore
└──────┬──────┘                   │  - Resume
       │ NO                       └─────────────
       ▼
┌─────────────┐
│   SYNC      │
│  - Network  │
│  - Update   │
└──────┬──────┘
       │
       ▼
    (back to OBSERVE)

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5. Configuration

5.1 Core Parameters

agent:
  id: auto-generated
  mode: full | light
  checkpoint_interval: 300  # seconds
  sync_interval: 60        # seconds

atomizer:
  confidence_threshold: 0.7
  max_tokens: 10000

discovery:
  min_coverage: 0.1
  simplicity_weight: 0.3
  min_support: 3

consensus:
  min_agents: 3
  independence_threshold: 0.8
  stability_window: 86400  # seconds

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6. Failure Modes

6.1 Crash Recovery

If agent process dies:

1. On restart, load last checkpoint

2. Replay events since checkpoint

3. Verify consistency

4. Resume operation

6.2 Network Partition

If disconnected from network:

1. Continue local operation

2. Accumulate local discoveries

3. Broadcast on reconnection

4. Resolve any conflicts

6.3 Data Corruption

If local state corrupted:

1. Attempt recovery from replicas

2. If unavailable, reset to genesis state

3. Rebuild from network consensus

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7. Security

7.1 Agent Identity

Each agent has a cryptographic identity:

• Public/private key pair

• Certificate from network (if applicable)

• Attestation of node capabilities

7.2 Communication Security

All network communication:

• Encrypted (TLS or similar)

• Authenticated (signatures)

• Forward-secret (session keys)

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8. Related Documents

• System Overview: Context for agent architecture

• Consensus Model: PoC specification

• Threat Model: Security analysis

• Protocol Specification: Message formats

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Document Version: 1.1 Last Updated: February 2026 Changes: Added Phase 3 features (Enhanced Symbolic Regression, Multi-Modal Input)